CategoricalCatalog.OneHotHashEncoding 方法

定义

命名空间:

Microsoft.ML

程序集:

Microsoft.ML.Transforms.dll

包:

Microsoft.ML v2.0.0

重载

OneHotHashEncoding(TransformsCatalog+CategoricalTransforms, InputOutputColumnPair[], OneHotEncodingEstimator+OutputKind, Int32, UInt32, Boolean, Int32)	创建一个 OneHotHashEncodingEstimator，它将指定的 columns 一个或多个输入文本列转换为基于哈希的单热编码矢量的多个列。
OneHotHashEncoding(TransformsCatalog+CategoricalTransforms, String, String, OneHotEncodingEstimator+OutputKind, Int32, UInt32, Boolean, Int32)	Create a OneHotHashEncodingEstimator, which converts a text column specified by inputColumnName into a hash-based one-hot encoded vector column named outputColumnName.

OneHotHashEncoding(TransformsCatalog+CategoricalTransforms, InputOutputColumnPair[], OneHotEncodingEstimator+OutputKind, Int32, UInt32, Boolean, Int32)

创建一个 OneHotHashEncodingEstimator，它将指定的 columns 一个或多个输入文本列转换为基于哈希的单热编码矢量的多个列。

public static Microsoft.ML.Transforms.OneHotHashEncodingEstimator OneHotHashEncoding (this Microsoft.ML.TransformsCatalog.CategoricalTransforms catalog, Microsoft.ML.InputOutputColumnPair[] columns, Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind outputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, int numberOfBits = 16, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0);

static member OneHotHashEncoding : Microsoft.ML.TransformsCatalog.CategoricalTransforms * Microsoft.ML.InputOutputColumnPair[] * Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind * int * uint32 * bool * int -> Microsoft.ML.Transforms.OneHotHashEncodingEstimator

<Extension()>
Public Function OneHotHashEncoding (catalog As TransformsCatalog.CategoricalTransforms, columns As InputOutputColumnPair(), Optional outputKind As OneHotEncodingEstimator.OutputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, Optional numberOfBits As Integer = 16, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0) As OneHotHashEncodingEstimator

参数

catalog

TransformsCatalog.CategoricalTransforms

转换目录

columns

InputOutputColumnPair[]

输入和输出列对。 输出列的数据类型将是 if outputKind 的向量Single，Indicator并且BinaryBag。 Key如果是outputKind，则输出列的数据类型将是标量输入列的键，或者在矢量输入列的情况下为键的向量。

outputKind

OneHotEncodingEstimator.OutputKind

转换模式。

numberOfBits

Int32

要哈希到的位数。 必须介于 1 和 30 之间（含限值）。

seed

UInt32

哈希种子。

useOrderedHashing

Boolean

是否应将每个术语的位置包含在哈希中。

maximumNumberOfInverts

Int32

在哈希处理期间，我们在原始值和生成的哈希值之间进行断定映射。 原始值的文本表示形式存储在新列的元数据的槽名称中。 因此，哈希可以将许多初始值映射到一个值。 maximumNumberOfInverts 指定映射到应保留的哈希的非重复输入值数的上限。 0 不保留任何输入值。 -1 保留映射到每个哈希的所有输入值。

返回

OneHotHashEncodingEstimator

示例

using System;
using Microsoft.ML;

namespace Samples.Dynamic.Transforms.Categorical
{
    public static class OneHotHashEncodingMultiColumn
    {
        public static void Example()
        {
            // Create a new ML context for ML.NET operations. It can be used for
            // exception tracking and logging as well as the source of randomness.
            var mlContext = new MLContext();

            // Get a small dataset as an IEnumerable.
            var samples = new[]
            {
                new DataPoint {Education = "0-5yrs", ZipCode = "98005"},
                new DataPoint {Education = "0-5yrs", ZipCode = "98052"},
                new DataPoint {Education = "6-11yrs", ZipCode = "98005"},
                new DataPoint {Education = "6-11yrs", ZipCode = "98052"},
                new DataPoint {Education = "11-15yrs", ZipCode = "98005"}
            };

            // Convert training data to IDataView.
            IDataView data = mlContext.Data.LoadFromEnumerable(samples);

            // Multi column example: A pipeline for one hot has encoding two
            // columns 'Education' and 'ZipCode'.
            var multiColumnKeyPipeline =
                mlContext.Transforms.Categorical.OneHotHashEncoding(
                    new[]
                    {
                        new InputOutputColumnPair("Education"),
                        new InputOutputColumnPair("ZipCode")
                    },
                    numberOfBits: 3);

            // Fit and Transform the data.
            IDataView transformedData =
                multiColumnKeyPipeline.Fit(data).Transform(data);

            var convertedData =
                mlContext.Data.CreateEnumerable<TransformedData>(transformedData,
                    true);

            Console.WriteLine(
                "One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.");

            // One Hot Hash Encoding of two columns 'Education' and 'ZipCode'.

            foreach (TransformedData item in convertedData)
                Console.WriteLine("{0}\t\t\t{1}", string.Join(" ", item.Education),
                    string.Join(" ", item.ZipCode));

            // We have 8 slots, because we used numberOfBits = 3.

            // 0 0 0 1 0 0 0 0                 0 0 0 0 0 0 0 1
            // 0 0 0 1 0 0 0 0                 1 0 0 0 0 0 0 0
            // 0 0 0 0 1 0 0 0                 0 0 0 0 0 0 0 1
            // 0 0 0 0 1 0 0 0                 1 0 0 0 0 0 0 0
            // 0 0 0 0 0 0 0 1                 0 0 0 0 0 0 0 1
        }

        private class DataPoint
        {
            public string Education { get; set; }

            public string ZipCode { get; set; }
        }

        private class TransformedData
        {
            public float[] Education { get; set; }

            public float[] ZipCode { get; set; }
        }
    }
}

注解

如果将多个列传递给估算器，则所有列都将在一次传递数据中进行处理。 因此，使用多个列指定一个估算器比指定一个包含单个列的估算器更有效。

OneHotHashEncoding(TransformsCatalog+CategoricalTransforms, String, String, OneHotEncodingEstimator+OutputKind, Int32, UInt32, Boolean, Int32)

Create a OneHotHashEncodingEstimator, which converts a text column specified by inputColumnName into a hash-based one-hot encoded vector column named outputColumnName.

public static Microsoft.ML.Transforms.OneHotHashEncodingEstimator OneHotHashEncoding (this Microsoft.ML.TransformsCatalog.CategoricalTransforms catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind outputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, int numberOfBits = 16, uint seed = 314489979, bool useOrderedHashing = true, int maximumNumberOfInverts = 0);

static member OneHotHashEncoding : Microsoft.ML.TransformsCatalog.CategoricalTransforms * string * string * Microsoft.ML.Transforms.OneHotEncodingEstimator.OutputKind * int * uint32 * bool * int -> Microsoft.ML.Transforms.OneHotHashEncodingEstimator

<Extension()>
Public Function OneHotHashEncoding (catalog As TransformsCatalog.CategoricalTransforms, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional outputKind As OneHotEncodingEstimator.OutputKind = Microsoft.ML.Transforms.OneHotEncodingEstimator+OutputKind.Indicator, Optional numberOfBits As Integer = 16, Optional seed As UInteger = 314489979, Optional useOrderedHashing As Boolean = true, Optional maximumNumberOfInverts As Integer = 0) As OneHotHashEncodingEstimator

参数

catalog

TransformsCatalog.CategoricalTransforms

转换目录。

outputColumnName

String

由转换 inputColumnName生成的列的名称。 此列的数据类型将是 if IndicatoroutputKindBag的向量Single，并且。Binary Key如果是outputKind，则此列的数据类型将是标量输入列的键，或者当矢量输入列时为键的向量。

inputColumnName

String

要转换的列的名称。 If set to null, the value of the outputColumnName will be used as source. 此列的数据类型可以是数值、文本、布尔 DateTime 值或 DateTimeOffset数值的标量或向量。

outputKind

OneHotEncodingEstimator.OutputKind

转换模式。

numberOfBits

Int32

要哈希到的位数。 必须介于 1 和 30 之间（含限值）。

seed

UInt32

哈希种子。

useOrderedHashing

Boolean

是否应将每个术语的位置包含在哈希中。

maximumNumberOfInverts

Int32

在哈希处理期间，我们在原始值和生成的哈希值之间进行断定映射。 原始值的文本表示形式存储在新列的元数据的槽名称中。因此，哈希可以将许多初始值映射到一个值。 maximumNumberOfInverts 指定映射到应保留的哈希的非重复输入值数的上限。 0 不保留任何输入值。 -1 保留映射到每个哈希的所有输入值。

返回

OneHotHashEncodingEstimator

示例

using System;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;

namespace Samples.Dynamic.Transforms.Categorical
{
    public static class OneHotHashEncoding
    {
        public static void Example()
        {
            // Create a new ML context for ML.NET operations. It can be used for
            // exception tracking and logging as well as the source of randomness.
            var mlContext = new MLContext();

            // Create a small dataset as an IEnumerable.
            var samples = new[]
            {
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "11-15yrs"}
            };

            // Convert training data to an IDataView.
            IDataView data = mlContext.Data.LoadFromEnumerable(samples);

            // A pipeline for one hot hash encoding the 'Education' column.
            var pipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(
                "EducationOneHotHashEncoded", "Education", numberOfBits: 3);

            // Fit and transform the data.
            IDataView hashEncodedData = pipeline.Fit(data).Transform(data);

            PrintDataColumn(hashEncodedData, "EducationOneHotHashEncoded");
            // We have 8 slots, because we used numberOfBits = 3.

            // 0 0 0 1 0 0 0 0
            // 0 0 0 1 0 0 0 0
            // 0 0 0 0 1 0 0 0
            // 0 0 0 0 1 0 0 0
            // 0 0 0 0 0 0 0 1

            // A pipeline for one hot hash encoding the 'Education' column
            // (using keying strategy).
            var keyPipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(
                "EducationOneHotHashEncoded", "Education",
                OneHotEncodingEstimator.OutputKind.Key, 3);

            // Fit and transform the data.
            IDataView hashKeyEncodedData = keyPipeline.Fit(data).Transform(data);

            // Get the data of the newly created column for inspecting.
            var keyEncodedColumn =
                hashKeyEncodedData.GetColumn<uint>("EducationOneHotHashEncoded");

            Console.WriteLine(
                "One Hot Hash Encoding of single column 'Education', with key " +
                "type output.");

            // One Hot Hash Encoding of single column 'Education', with key type output.

            foreach (uint element in keyEncodedColumn)
                Console.WriteLine(element);

            // 4
            // 4
            // 5
            // 5
            // 8
        }

        private static void PrintDataColumn(IDataView transformedData,
            string columnName)
        {
            var countSelectColumn = transformedData.GetColumn<float[]>(
                transformedData.Schema[columnName]);

            foreach (var row in countSelectColumn)
            {
                for (var i = 0; i < row.Length; i++)
                    Console.Write($"{row[i]}\t");
                Console.WriteLine();
            }
        }

        private class DataPoint
        {
            public string Education { get; set; }
        }
    }
}